ARGO
Nicholas Baetge
2/6/2020
Intro
This document highlights how MLDs were calculated from the ARGO floats deployed on the NAAMES cruises.
- Input data, which were the level 2 processed data, were pulled from the NAAMES data page
- MLDs were calculated using the N2 buoyancy frequency and are defined as the depth below 5 m at which N2 > |stdev(N2)|
Import and Wrangle Data
#load each float dataset
lovbio014b <- read_csv("~/Google Drive File Stream/Shared drives/NAAMES_Carlson/DATA/FINAL/MANUSCRIPT_DATA/Export_MS/Input/Argo/lovbio014b_profiles.csv") %>%
mutate(float = "lovbio014b") %>% select(float, id, datetime:s, chla_adj) %>%
rename(chla = chla_adj)
lovbio030b <- read_csv("~/Google Drive File Stream/Shared drives/NAAMES_Carlson/DATA/FINAL/MANUSCRIPT_DATA/Export_MS/Input/Argo/lovbio030b_profiles.csv") %>%
mutate(float = "lovbio030b") %>% select(float, id, datetime:s, chla_adj) %>%
rename(chla = chla_adj)
lovbio032b <- read_csv("~/Google Drive File Stream/Shared drives/NAAMES_Carlson/DATA/FINAL/MANUSCRIPT_DATA/Export_MS/Input/Argo/lovbio032b_profiles.csv") %>%
mutate(float = "lovbio032b") %>% select(float, id, datetime:s, chla_adj) %>%
rename(chla = chla_adj)
metbio003d <- read_csv("~/Google Drive File Stream/Shared drives/NAAMES_Carlson/DATA/FINAL/MANUSCRIPT_DATA/Export_MS/Input/Argo/metbio003d_profiles.csv") %>%
mutate(float = "metbio003d") %>% select(float, id, datetime:s, chla_adj) %>%
rename(chla = chla_adj)
metbio010d <- read_csv("~/Google Drive File Stream/Shared drives/NAAMES_Carlson/DATA/FINAL/MANUSCRIPT_DATA/Export_MS/Input/Argo/metbio010d_profiles.csv") %>%
mutate(float = "metbio010d") %>% select(float, id, datetime:s, chla_adj) %>%
rename(chla = chla_adj)
#this dataset contained NANs which are problematic for the potential temperature and n2 calculations. thus, they are removed here.
metbio010d <- metbio010d[complete.cases(metbio010d),]
n0572 <- read_csv("~/Google Drive File Stream/Shared drives/NAAMES_Carlson/DATA/FINAL/MANUSCRIPT_DATA/Export_MS/Input/Argo/n0572_profiles.csv") %>%
mutate(float = "n0572") %>% select(float, id, datetime:s, o2_c, chla_adj) %>%
rename(chla = chla_adj)
n0573 <- read_csv("~/Google Drive File Stream/Shared drives/NAAMES_Carlson/DATA/FINAL/MANUSCRIPT_DATA/Export_MS/Input/Argo/n0573_profiles.csv") %>%
mutate(float = "n0573") %>% select(float, id, datetime:s, o2_c, chla_adj) %>%
rename(chla = chla_adj)
n0574 <- read_csv("~/Google Drive File Stream/Shared drives/NAAMES_Carlson/DATA/FINAL/MANUSCRIPT_DATA/Export_MS/Input/Argo/n0574_profiles.csv") %>%
mutate(float = "n0574") %>% select(float, id, datetime:s, o2_c, chla_adj) %>%
rename(chla = chla_adj)
n0646 <- read_csv("~/Google Drive File Stream/Shared drives/NAAMES_Carlson/DATA/FINAL/MANUSCRIPT_DATA/Export_MS/Input/Argo/n0646_profiles.csv") %>%
mutate(float = "n0646") %>% select(float, id, datetime:s, o2_c, chla_adj) %>%
rename(chla = chla_adj)
n0647 <- read_csv("~/Google Drive File Stream/Shared drives/NAAMES_Carlson/DATA/FINAL/MANUSCRIPT_DATA/Export_MS/Input/Argo/n0647_profiles.csv") %>%
mutate(float = "n0647") %>% select(float, id, datetime:s, o2_c, chla_adj) %>%
rename(chla = chla_adj)
n0648 <- read_csv("~/Google Drive File Stream/Shared drives/NAAMES_Carlson/DATA/FINAL/MANUSCRIPT_DATA/Export_MS/Input/Argo/n0648_profiles.csv") %>%
mutate(float = "n0648") %>% select(float, id, datetime:s, o2_c, chla_adj) %>%
rename(chla = chla_adj)
n0846 <- read_csv("~/Google Drive File Stream/Shared drives/NAAMES_Carlson/DATA/FINAL/MANUSCRIPT_DATA/Export_MS/Input/Argo/n0846_profiles.csv") %>%
mutate(float = "n0846") %>% select(float, id, datetime:s, o2_c, chla_adj) %>%
rename(chla = chla_adj)
n0847 <- read_csv("~/Google Drive File Stream/Shared drives/NAAMES_Carlson/DATA/FINAL/MANUSCRIPT_DATA/Export_MS/Input/Argo/n0847_profiles.csv") %>%
mutate(float = "n0847") %>% select(float, id, datetime:s, o2_c, chla_adj) %>%
rename(chla = chla_adj)
n0848 <- read_csv("~/Google Drive File Stream/Shared drives/NAAMES_Carlson/DATA/FINAL/MANUSCRIPT_DATA/Export_MS/Input/Argo/n0848_profiles.csv") %>%
mutate(float = "n0848") %>% select(float, id, datetime:s, o2_c, chla_adj) %>%
rename(chla = chla_adj)
n0849 <- read_csv("~/Google Drive File Stream/Shared drives/NAAMES_Carlson/DATA/FINAL/MANUSCRIPT_DATA/Export_MS/Input/Argo/n0849_profiles.csv") %>%
mutate(float = "n0849") %>% select(float, id, datetime:s, o2_c, chla_adj) %>%
rename(chla = chla_adj)
n0850 <- read_csv("~/Google Drive File Stream/Shared drives/NAAMES_Carlson/DATA/FINAL/MANUSCRIPT_DATA/Export_MS/Input/Argo/n0850_profiles.csv") %>%
mutate(float = "n0850") %>% select(float, id, datetime:s, o2_c, chla_adj) %>%
rename(chla = chla_adj)
n0851 <- read_csv("~/Google Drive File Stream/Shared drives/NAAMES_Carlson/DATA/FINAL/MANUSCRIPT_DATA/Export_MS/Input/Argo/n0851_profiles.csv") %>%
mutate(float = "n0851") %>% select(float, id, datetime:s, o2_c, chla_adj) %>%
rename(chla = chla_adj)
n0852 <- read_csv("~/Google Drive File Stream/Shared drives/NAAMES_Carlson/DATA/FINAL/MANUSCRIPT_DATA/Export_MS/Input/Argo/n0852_profiles.csv") %>%
mutate(float = "n0852") %>% select(float, id, datetime:s, o2_c, chla_adj) %>%
rename(chla = chla_adj)
#combine all the float data
#this first set of floats don't have O2 data
collab_floats.df <- rbind(lovbio014b, lovbio030b, lovbio032b, metbio003d, metbio010d) %>%
rename(profile = id)
naames_floats.df <- rbind(n0572, n0573, n0574, n0647, n0648, n0846, n0847, n0848, n0849, n0850, n0851, n0852) %>%
rename(profile = id)
all_floats.df <- naames_floats.df %>%
select(-o2_c) %>%
rbind(., collab_floats.df) %>%
drop_na() Calculations
MLDs
float_mld.df <- all_floats.df %>%
group_by(float, profile, datetime) %>%
mutate(sigmaT = swSigmaTheta(s, temperature = t, pressure = p, referencePressure = 0),
N2 = swN2(pressure = p, sigmaTheta = sigmaT),
Date = as.Date(datetime, "%m/%d/%y"),
Month = month(Date)) %>%
#include only depths below 5 m and where N2 is > abs(stdev(N2))
filter(p > 5 & N2 > abs(sd(N2))) %>%
filter(p == min(p)) %>% #report the shallowest depth at which the above condition is met
rename(MLD = p) %>%
ungroup() %>%
select(float, profile, datetime, Date, Month, lat:MLD) %>%
#set longitudinal and latitudinal bounds based on the extent of the NAAMES region
filter(between(lon, -47, -38),
between(lat, 39,57)) %>%
mutate(bin = round(lat)) %>%
arrange(bin, Date) %>%
select(float:datetime, lat, lon, bin, MLD) %>%
group_by(bin) %>%
add_tally() %>%
rename(nperbin = n) %>%
ungroup()
mld_winter <- float_mld.df %>%
select(float, profile, datetime, bin) %>%
mutate(month = month(datetime)) %>%
filter(month %in% c(11,12,1,2,3,4)) %>%
select(float, profile, month, bin) %>%
group_by(bin) %>%
add_tally() %>%
rename(nperwinter = n) %>%
select(bin, nperwinter) %>%
distinct() %>%
ungroup()
float_mld.df <- left_join(float_mld.df, mld_winter)Chl
float_chl.df <- all_floats.df %>%
#set longitudinal and latitudinal bounds based on the extent of the NAAMES region
filter(between(lon, -47, -38),
between(lat, 39,57)) %>%
group_by(float, profile, datetime) %>%
select(float:t, chla) %>%
#correct the chlorophyll data for each profile by subtracting the min from each observation
mutate(min_fl = min(chla),
cor_fl = chla - min_fl) %>%
select(float:t, cor_fl)Max MLD
float_combined.df <- left_join(float_mld.df, float_chl.df) %>%
mutate(decimaldate = decimal_date(datetime)) %>%
ungroup() %>%
select(float, profile, datetime, decimaldate, lat, lon, bin, MLD, nperbin, nperwinter, p, t, cor_fl) %>%
drop_na() %>%
arrange(bin, decimaldate)
max_mld.df <- float_combined.df %>%
arrange(bin, datetime) %>%
group_by(bin) %>%
select(float:nperwinter) %>%
distinct() %>%
mutate(max_mld = max(MLD)) %>%
filter(MLD == max_mld) %>%
select(-MLD) %>%
distinct(.) %>%
mutate(date = date_decimal(decimaldate),
mon = month(date, label=TRUE, abbr=TRUE),
year = year(date)) %>%
arrange(bin) %>%
select(bin, nperbin, nperwinter, max_mld, float, profile, mon, year, lat, lon )
saveRDS(max_mld.df, "~/Google Drive File Stream/Shared drives/NAAMES_Carlson/DATA/FINAL/MANUSCRIPT_DATA/Export_MS/Output/max_mld.rds")| bin | nperbin | nperwinter | max_mld | float | profile | mon | year | lat | lon |
|---|---|---|---|---|---|---|---|---|---|
| 39 | 52 | 52 | 294.0 | n0851 | 152 | Apr | 2018 | 39.18970 | -40.29010 |
| 40 | 17 | 17 | 484.0 | n0852 | 113 | Mar | 2018 | 39.85230 | -42.13640 |
| 41 | 49 | 26 | 478.0 | n0852 | 105 | Mar | 2018 | 41.15090 | -41.49080 |
| 42 | 114 | 61 | 418.0 | n0852 | 103 | Feb | 2018 | 41.57950 | -41.06190 |
| 42 | 114 | 61 | 418.0 | n0852 | 150 | Apr | 2018 | 42.16620 | -42.20900 |
| 43 | 66 | 51 | 416.0 | n0852 | 148 | Apr | 2018 | 42.91960 | -43.86390 |
| 43 | 66 | 51 | 416.0 | n0852 | 149 | Apr | 2018 | 42.56660 | -43.01800 |
| 44 | 173 | 89 | 368.0 | n0850 | 100 | Feb | 2018 | 44.39350 | -43.68720 |
| 45 | 113 | 49 | 404.0 | n0850 | 96 | Feb | 2018 | 45.09000 | -42.95190 |
| 46 | 78 | 37 | 416.0 | n0572 | 49 | Feb | 2016 | 45.94860 | -38.82450 |
| 47 | 132 | 9 | 336.0 | n0849 | 106 | Mar | 2018 | 47.21110 | -39.06920 |
| 48 | 90 | 27 | 508.0 | n0849 | 105 | Mar | 2018 | 48.01290 | -38.83260 |
| 49 | 92 | 46 | 448.0 | n0647 | 94 | Feb | 2017 | 48.81720 | -41.74480 |
| 50 | 181 | 159 | 406.0 | n0647 | 97 | Mar | 2017 | 50.42140 | -40.91630 |
| 51 | 177 | 165 | 386.0 | n0849 | 96 | Feb | 2018 | 50.88810 | -40.45680 |
| 52 | 348 | 274 | 330.0 | n0849 | 90 | Feb | 2018 | 51.60300 | -44.00300 |
| 53 | 375 | 223 | 230.5 | lovbio014b | 18400 | Mar | 2016 | 53.35727 | -40.07517 |
| 54 | 219 | 158 | 222.7 | lovbio014b | 17300 | Dec | 2015 | 54.07244 | -44.11888 |
| 55 | 39 | 32 | 241.3 | lovbio030b | 21500 | Jan | 2016 | 54.82318 | -45.90412 |
| 56 | 54 | 11 | 109.6 | lovbio014b | 16800 | Nov | 2015 | 55.59245 | -44.25288 |
| 57 | 56 | 5 | 284.0 | n0648 | 205 | Feb | 2018 | 56.74900 | -46.73290 |
ODV style plot
Interpolate temperature data
#subset the temperature and the mld data separately
t.df <- float_combined.df %>%
#deepest mixed layer observed from floats in NAAMES region was 508
filter(between(p, 0, 550)) %>%
select(bin, decimaldate, p, t) %>%
arrange(decimaldate, bin) %>%
group_by(bin)
mld.df <- float_combined.df %>%
filter(between(p, 0, 550)) %>%
select(bin, decimaldate, MLD) %>%
arrange(decimaldate, bin) %>%
group_by(bin)
#split chl data into a list by latitudinal bin
t.list <- split(t.df, t.df$bin)
#create function to interpolate chl
interp.func <- function(casper) {
dog <- casper %>%
ungroup() %>%
#the interpolation requires that the dataframe be comprised of only 3 columns (x, y, z)
select(-bin)
mba <- mba.surf(dog, no.X = 300, no.Y = 300, extend = T)
dimnames(mba$xyz.est$z) <- list(mba$xyz.est$x, mba$xyz.est$y)
mba <- melt(mba$xyz.est$z, varnames = c('decimaldate', 'p'), value.name = 't') %>%
filter(p > 0)
}
#apply the interpolation function to the list
interpolated_t.list <- lapply(t.list, interp.func)
#convert the list into a df for plotting
interpolated_t.df <- data.frame(rbindlist(interpolated_t.list, idcol = T)) %>%
mutate(.id = as.numeric(.id)) %>%
rename(bin = .id) %>%
arrange(decimaldate, bin) %>%
group_by(bin) Plot
